International Journal of Science and Research (IJSR) ISSN: 2319-7064
ResearchGate Impact Factor (2018): 0.28 | SJIF (2018): 7.426
Volume 8 Issue 11, November 2019
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
Parametric Optimization of Process Parameters of
EDM for SS304 by Using Design of Experiments
Method
Narendra Kumar Patel1, Harshwardhan Singh Rathore
2
1Assistant Professor, Department of Mechanical Engineering, Dr. C.V. Raman University, Bilaspur, Chhattisgarh, India
2M.Tech. Scholar, Department of Mechanical Engineering, Dr. C.V. Raman University, Bilaspur, Chhattisgarh, India
Abstract: EDM has become an important and cost-effective method of machining extremely tough and brittle electrically conductive
materials. It is widely used in the process of making moulds and dies and sections of complex geometry and intricate shapes. The work-
piece material has been selected for this experiment is AISI304 Stainless steel taking into account its wide usage in industrial
applications. In today’s world 304 Stainless steel contributes to almost half of the world’s production and consumption for industrial
purposes. The input variable parameters are discharge current, pulse on time and pulse off time. Taguchi method is applied to create an
L18 orthogonal array of input variables using the Design of Experiments (DOE). The effect of the variable parameters mentioned
above upon machining characteristics such as Material Removal Rate (MRR), will be carried out for optimization and investigation.
The tool material will be chosen as copper electrode whose shape and size is cylindrical bar of 10 mm diameter. And the work-piece
material is 5mm thick 304 stainless steel plate.
Keywords: Taguchi method, MRR, electrically conductive materials etc.
1. Introduction
Electrical Discharge Machining, commonly known as EDM
is a non-conventional machining method used to remove
material by a number of repetitive electrical discharges of
small duration and high current density between the work
piece and the tool. EDM is an important and cost-effective
method of machining extremely tough and brittle electrically
conductive materials. In EDM, since there is no direct
contact between the work piece and the electrode, hence
there are no mechanical forces existing between them. Any
type of conductive material can be machined using EDM
irrespective of the hardness or toughness of the material.
In this process the material is removed from the work piece
due to erosion caused by rapidly recurring electrical spark
discharge between the work piece and the tool electrode.
There is a small gap between the tool and the work piece.
The work piece and tool both are submerged in dielectric
fluid, commonly used are EDM oil, de-ionized water, and
kerosene.
Basically there are two types of EDM: Die-sinking EDM and
Wire-cut EDM
1.1. Die-sinking EDM
Die-sinking EDM, also known as Volume EDM or cavity
type EDM consists of an electrode and a work piece which is
submerged in an insulating fluid such as oil or other
dielectric fluids.
1.2. Wire-cut EDM
Wire-cut EDM, also known as Spark EDM is mostly used
when low residual stresses are required, as it does not needs
high cutting forces for removal of material.
2. Methodology Used
For fabricating arrayed structures, the output response such
as MRR was measured. The measurement approach of these
output response was discussed as below:
2.1. MRR is defined as the ratio of the difference of weight
of the work piece before and after machining to the
machining time and density of the material as shown in
below:
MRR = mm3/min.
Where,
Wi = weight before machining (gm),
Wf = weight after machining (gm),
D = density of work piece material (gm/mm3)
t = time consumed for machining (min.).
2.2. Performance evaluation using Taguchi’s S/N ratio
Taguchi method uses a statistical tool to measure
performance characteristics known as signal-to-noise (S/N)
ratio to find out the optimum settings for the design
parameters and it (S/N ratio) consists of both the mean and
the variability of performance characteristics. This method
tries to select the design parameter in order to maximize the
S/N ratio because higher the S/N ratio the more balanced the
adoptable is the performance quality. There are three
criterions of signal-to-noise ratios which are lower-the-better
(LTB), the higher-the-better (HTB), and nominal-the-best
(NTB). In present work one response is considered material
removal rate (MRR). Material removal rate is need to be
maximize. So, for calculating S/N ratio of material removal
rate HTB criterion should be selected. The equation for
aforesaid two criterions is given below:
Paper ID: ART20202435 10.21275/ART20202435 56
International Journal of Science and Research (IJSR) ISSN: 2319-7064
ResearchGate Impact Factor (2018): 0.28 | SJIF (2018): 7.426
Volume 8 Issue 11, November 2019
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
Lower-is-Better (LTB) criterion:
S/N ratio = − 10 log(
Higher-is-Better (HTB) criterion:
S/N ratio = − 10 log(
Here, y is average of all observed value and n is number of
observations. So this method is useful only for optimization
of a single performance characteristic. Optimization of
design parameters with more than one performance
characteristics are still an area of research.
3. Experimentation
3.1 Copper Electrode Properties
Copper and copper alloys have better EDM wear resistance
than brass, but are more difficult to machine than either brass
or graphite. It is also more expensive than graphite. Copper
is, however, a common base material because it is highly
conductive and strong. It is useful in the EDM machining of
tungsten carbide, or in applications requiring a fine finish.
Copper tungsten materials are composites of tungsten and
copper. They are produced using powder metallurgy
processes. Copper tungsten is very expensive compared to
other electrode materials, but is useful for making deep slots
under poor flushing conditions and in the EDM machining of
tungsten carbide. Copper tungsten materials are also used in
resistance welding electrodes and some circuit breaker
applications.
Figure 1: Copper Electrode used in experiment
3.2 Chosen Work-piece Material
AISI304 Stainless Steel is still sometimes referred to by its
old name 18/8 which is derived from the nominal
composition of type 304 being 18% chromium and 8%
nickel. Stainless steel 304 has excellent corrosion resistance
in a wide variety of environments and when in contact with
different corrosive media. Pitting and crevice corrosion can
occur in environments containing chlorides. Stress corrosion
cracking can occur at temperatures over 60°C.
Stainless steel 304 has good resistance to oxidation in
intermittent service up to 870°C and in continuous service to
925°C. However, continuous use at 425-860°C is not
recommended if corrosion resistance in water is required. In
this instance 304L is recommended due to its resistance to
carbide precipitation.
Figure 2: Experimental setup of Copper tool and Work-
piece
Figure 3: Display Panel of EDM for taken readings
The above display panel is associated with the control of
CNC operated EDM where the values are feed at allocated
parameters. And after machining the readings are noted
down.
In the current study, the machining was done by choosing
discharge current (Ip), pulse-on time (Ton) and pulse-off
time (Toff) as input parameters and the other parameters
such as duty factor (τau) equal to 80 % and gap voltage of 50
V are kept constant throughout the experiment.
Table 1: Allocated Values of EDM parameters and their
levels Parameters Units Level 1 Level 2 Level 3
A Discharge current (Ip) Amps. 10 6 3
B Pulse on time (Ton) µs 400 200 100
C Pulse off time (Toff) µs 32 20 10
4. Results and Discussion
Based on the above limitation of EDM the designed
experiment is performed and calculation is done. And found
that which combination of machined parameters is best
suited for industry application to get higher material removal
rate over the work-piece.
Paper ID: ART20202435 10.21275/ART20202435 57
International Journal of Science and Research (IJSR) ISSN: 2319-7064
ResearchGate Impact Factor (2018): 0.28 | SJIF (2018): 7.426
Volume 8 Issue 11, November 2019
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
Table 2: Calculation table by DoE (Design of experiment)
Matrix of L18 Orthogonal array (OA) Sl.
No.
Current (Ip)
(Amperes)
Ton
(micro seconds)
Toff
(micro seconds)
Calculation of
MRR (mm3/Min.)
1 10 400 32 0.0156
2 10 200 20 0.0105
3 10 100 10 0.0625
4 6 400 32 0.0084
5 6 200 20 0.0083
6 6 100 10 0.0028
7 3 400 20 0.0027
8 3 200 10 0.0018
9 3 100 32 0.0015
10 10 100 10 0.0133
11 10 200 32 0.0041
12 10 400 20 0.0189
13 6 100 20 0.0027
14 6 200 10 0.0055
15 6 400 32 0.0067
16 3 100 10 0.0008
17 3 200 32 0.0004
18 3 400 20 0.0047
Figure 4: Image of work-piece after L18 experiment in
EDM
4.1 Influences on MRR
The S/N ratios for MRR are calculated as given in Equation.
Taguchi method is used to analysis the result of response of
machining parameter for larger is better criteria.
4.2 Model Analysis of MRR
The coefficients of model for S/N ratios for MRR are shown
in Table 4.4. The parameter R-Sq describes the amount of
variation observed in MRR is explained by the input factors.
R-Sq = 64.7 % indicate that the model is able to predict the
response with high accuracy. R-Sq(sdj) is a modified R-Sq
that has been adjusted for the number of terms in the model.
If unnecessary terms are included in the model, R-Sq can be
artificially high, but R-Sq(sdj) = 38.2 %. may get smaller.
The standard deviation of errors in the modeling, S=
0.007752 . Comparing the p-value to a commonly used α-
level = 0.05, it is found that if the p-value is less than or
equal to α, it can be concluded that the effect is significant
(shown in bold), otherwise it is not significant.
Table 3: Coefficients of model for S/N ratios A B C MRR SNRA1
10 400 32 0.0156 -36.1375
10 200 20 0.0105 -39.5762
10 100 10 0.0625 -34.705
6 400 32 0.0084 -42.6066
6 200 20 0.0083 -41.6184
6 100 10 0.0028 -51.0568
3 400 20 0.0027 -49.601
3 200 10 0.0018 -54.8945
3 100 32 0.0015 -56.4782
10 100 10 0.0133
10 200 32 0.0041 -47.7443
10 400 20 0.0189 -34.4708
6 100 20 0.0027 -51.3727
6 200 10 0.0055 -45.1927
6 400 32 0.0067
3 100 10 0.0008 -61.9382
3 200 32 0.0004 -67.9588
3 400 20 0.0047
S = 0.007752 R-Sq = 64.7% R-Sq(adj) = 38.2%
From the Analysis of variance studied for the effect of
factors on MRR is indicating in Table 4 which obviously
shows that the discharge current is the most significant factor
while machining of AISI 304 SS with copper tool. After that
pulse off time is an important parameter and pulse on time is
significant factor during machining. Figure shows that the
main effects of S/N ratios on MRR by the factor. For this
case “higher is better” is chosen.
Table 4: ANOVA Table Source DF Seq SS Adj SS Adj MS F P
A 2 0.000678 0.000706 0.000353 5.87 0.027
B 2 0.0001 0.000096 0.000048 0.8 0.484
C 2 0.000103 0.000103 0.000051 0.85 0.461
Residual Error 8 0.000481 0.000481 0.00006
Total 14 0.001362
The residual plot of MRR is shown in Fig 5. This design is
helpful to determine whether the model meets the
assumptions of the analysis. The residual plots in the graph
and the interpretation of each residual plot indicate below:
a) Normal probability plot indicates the data are usually
distributed and the variables are influencing the response.
Outliers don’t exist in the data, because standardized
residues are between -0.01 and 0.01.
b) Residuals versus fitted values indicate the variance is
constant and a nonlinear relationship exists.
c) Histogram proves the data are not skewed.
d) Residuals versus order of the data indicate that there are
systematic effects in the data due to time or data
collection order.
Paper ID: ART20202435 10.21275/ART20202435 58
International Journal of Science and Research (IJSR) ISSN: 2319-7064
ResearchGate Impact Factor (2018): 0.28 | SJIF (2018): 7.426
Volume 8 Issue 11, November 2019
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
Figure 5: Residual plot of MRR
During the process of Electrical discharge machining, the
influence of various machining parameter like Ip, Ton and
Toff has significant effect on MRR, as shown in main effect
plot for S/N ratio of MRR in Fig 6. The discharge current
(Ip) is directly proportional to MRR in the range of 3A to
10A. This is expected because an increase in pulse current
produces strong spark, which produces the higher
temperature, causing more material to melt and erode from
the work piece. Besides, it is clearly evident that the other
factor does not influence much as compared to Ip. But, with
increase in discharge current from 3A to 10A MRR increases
slightly. However, MRR decreases monotonically with the
increase in pulse off time.
Figure 6: Main effects plot on S/N ratio on MRR by the
factor
5. Conclusions
In the current study on the outcome of machining response of
MRR of the AISI grade 304 stainless steel plate by means of
the cylindrical formed copper tool with external flush system
tool has been experimented for EDM process. The
experiments were conducted under various parameter set of
Discharge Current (Ip), Pulse On-Time (Ton), and Pulse
Off-Time (Toff) of the tool. L-18 OA based on Taguchi
design was performed for Minitab software was used for
examination the result and this response was partially
validated experimentally.
1) Finding the result of MRR discharge current is most
influencing factor and then pulse duration time. MRR
increased with the discharge current (Ip). As the pulse
duration extended, the MRR decreases monotonically.
2) The experiment of run number 3 gives the higher material
removal rate based on the combination of controlled
parameters of discharge current 10 amperes, pulse on
time is 100 micro-seconds and pulse off time is 10 micro-
seconds.
References
[1] Rao P.S., Kumar J.S, Reddy K., Reddy B., “Parametric
Study of Electric Discharge Machining of AISI 304
Stainless Steel,” International Journal of Engineering
Science and Technology, Vol. 2(8), pp. 3535-3550,
2010.
[2] Rahman M.M., Khan M.A.R., Kadirgama K., Noor
M.M. and Bakar R.A., “Experimental investigation into
Electric Discharge Machining of Stainless Steel 304,”
Journal of Applied Sciences, pp. 549-554, 2011.
[3] Iqbal AKM A., Khan A.A., “Optimization of process
parameters on EDM milling of stainless steel AISI
304,” Advance Materials Research, Vols. 264-265, pp.
979-984, 2011.
[4] Abbas Md. N., Solomon D.G., Bahari Md.F., “A review
on current research trends in electrical discharge
machining (EDM),” International Journal of Machine
tool and Manufacture, 47, pp. 1214-1228, 2007.
[5] Reza M.S., Hamdi M., Tomadi S.H., Ismail A.R.,
Optimization of control parameters for EWR in
injection flushing type of EDM on stainless steel 403
workpiece, World Academy of Science, Engineering
and Technology, 72, 2010.
[6] Dewangan S.K., "Experimental Investigation of
Machining parameters for EDM using U-shaped
Electrode of AISI P20 tool steel," M-Tech Thesis
2010,
[Online].Available:http://ethesis.nitrkl.ac.in/2071/1/The
sis_EDM.pdf.
[7] Mishra P., " Multi-objective optimization in electro
discharge machining of al/b4c metal matrix
composites," M-Tech Thesis 2015, [Online].Available: http://ethesis.nitrkl.ac.in/7867/1/2015_MT_Multi-
Objective_MISHRA.pdf.
[8] Patel N.K., “Parametric Optimization of Process
Parameters For EDM of Stainless Steel 304,” M-Tech
Thesis 2015, [Online].Available: http://ethesis.nitrkl.ac.in/6179/1/E-36.pdf.
[9] Dewngan. S, Biwas. C.K., Gangopadhyay. S,” Optimization of EDM process parameters using PCA
based Grey Relation Investigation,” Procidia Mater.
Sci., pp. 1091-1096, 2014.
[10] Sohani M.S., Gaitonde V.N., Siddeswarappa B. and
Deshpande A.S., “Investigations into the effect of tool
shapes with size factor consideration in sink electrical
discharge machining (EDM) process,” International
Journal of Advance Manufacturing Technology, 45,
pp.1131–1145, 2009.
Paper ID: ART20202435 10.21275/ART20202435 59
International Journal of Science and Research (IJSR) ISSN: 2319-7064
ResearchGate Impact Factor (2018): 0.28 | SJIF (2018): 7.426
Volume 8 Issue 11, November 2019
www.ijsr.net Licensed Under Creative Commons Attribution CC BY
Author Profile
Narendra Kumar Patel received the B.E. in
Mechanical Engineering from CSVTU Bhilai and
M.Tech. degrees in Production Engineering from
National Institute of Technology Rourkela in 2011 and
2014, respectively. He now with Dr.C.V. Raman
University, Bilaspur, Chhattisgarh as assistant professor in the
department of mechanical engineering.
Paper ID: ART20202435 10.21275/ART20202435 60